noise-lab.github.io/recognition.html at master · noise-lab/noise-lab.github.io · GitHub

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<!DOCTYPE html>
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<head>
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  <title>Activity Recognition</title>

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    <h2 class="mt-4 mb-3"> Activity Recognition from Network Traffic</h2>

    <p>
    Our research group discovered that data transmitted by smart home devices
    could reveal important information about the location and activities of
    users. Our work has shed light on the security and privacy risks of
    unencrypted traffic from smart home IoT devices, which ultimately led to
    the development of secure, encrypted transport from smart home devices.
    </p>

    <p>
    Our ongoing research is exploring how the network traffic
    transmitted from smart home IoT devices can offer <b>benefits</b> by
    allowing privacy-preserving, non-invasive sensing and inference of human
    activities within the home. We are currently exploring applications of
    activity recognition to various domains in health, medicine, and education, with
    applications to aging populations, childhoold development, and students.
    </p>

    <h4 class="mt-4 mb-3">Smart Home Activity Recognition</h4>
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        <p>
        The growing market for smart home IoT devices promises new
        conveniences for consumers while presenting challenges for
        preserving privacy within the home.
        Eavesdroppers can measure Internet
        traffic rates from smart home devices and infer
        in-home behaviors.
        </p>

        <p>
        In the Internet of Things lab at the Center for Data and Computing, we
        are exploring how network traffic in connected environments correlated
        with human behavior, and how those correlations can provide important
        information about personal health, education, and other factors in a
        privacy-preserving manner. The CDAC Internet of Things (IoT) Lab is a
        resource for UChicago students and faculty to experiment with the
        latest devices and datasets for research and applications.
        </p>
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        <img class="img-fluid rounded mb-4" src="./images/iot-lab.jpg" alt="">
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    <h4 class="mt-4 mb-3">Automated Labeling of Human Activities</h4> <div
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        <p>
            In the IoT Lab, we are
            continuously and automatically collecting traffic from IoT
            devices, aligning this data with video captures, and annotating
            this data with appropriate descriptors, including: the particular
            type of device, which activity the device is performing, and
            whether particular network patterns result from a security
            incident, such as device compromise.
        </p>

        <p>
            We are inspired by the area of computer vision,
            whose golden age was made possible by the modern proliferation of essential
            training data, and by the centralization of training data though repositories
            such as Image-Net and CIFAR. Our work aims to lay the first
            steps towards replicating such success in machine learning for the Internet of
            Things. Our initial focus is consumer IoT devices in smart homes, but
            we intend to generalize our approach to other IoT devices,
            including those in industrial, enterprise, and municipal (i.e., smart cities)
            networks.
        </p>

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    <h4 class="mt-4 mb-3">Selected Publications </h4>
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        <ul>
            <li>
            IoT Inspector: Crowdsourcing Labeled Network Traffic from Smart Home Devices at Scale <br />
            Danny Yuxing. Huang, Noah Apthorpe, Gunes Acar, Frank Li, and Nick Feamster. <br />
            Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT / Ubicomp). 2020.<br />
            </li>
            <p />

           <li>
            Cleartext Data Transmissions in Consumer IoT Medical Devices<br />
            Daniel Wood, Noah Apthorpe, Nick Feamster.<br />
            Workshop on Internet of Things Security and Privacy (IoT S&P).  2017
            </li>
            <p />

            <li>
                Instrumenting Home Networks<br />
                Ken Calvert,  W. Keith Edwards, Nick Feamster, Rebecca E. Grinter, Ye Deng, and Xuzi Zhou. <br />
                ACM SIGCOMM Computer Communication Review 41, no. 1 (2011): 84-89.
            </li>

        </ul>
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          <a
              href="https://www.nytimes.com/2020/01/07/opinion/location-tracking-privacy.html"><img class="img-fluid" src="images/nyt.jpg" alt=""></a>
      </div>
      <div class="col-lg-2 col-sm-4 mb-4">
          <a href="https://www.sciencefriday.com/segments/smart-tv-roku-spying/"><img class="img-fluid" src="images/npr.png" alt=""></a>
      </div>
      <div class="col-lg-2 col-sm-4 mb-4">
          <a href="https://www.washingtonpost.com/technology/2019/09/18/you-watch-tv-your-tv-watches-back/?noredirect=on"> <img class="img-fluid" src="images/wapo.jpg" alt=""></a>
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